A Machine Learning Approach for Automated Fine-Tuning of Semiconductor Spin Qubits

In the search for a technology best suited for quantum computation, spin qubits based on gate-defined quantum dots have demonstrated very favorable properties, one remaining challenge is their tuning into a suitable operating regime. Since this requires accurate tuning of the voltages applied to all electrostatic gates, this is a time-consuming procedure [1]. Thus, the automation of these tuning procedures is a necessary requirement for the operation of a quantum processor based on gate-defined quantum dots, which is yet to be fully addressed.

We present an algorithm for the automated fine-tuning of quantum dots, and benchmark its performance by tuning tunnel and lead coupling on a GaAs singlet triplet qubit. We employ a Bayesian approach called Kalman filter to estimate the gradients of the parameters of interest as function of gate voltages. Our benchmarks demonstrate the ability of reaching the operation regime within 3 to 5 iterations, corresponding to 10 to 15 minutes of lab-time.

¹T. Botzem et al., “Tuning methods for semiconductor spin–qubits,” , 1–12 (2018), arXiv:1801.0375